Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations500
Missing cells70
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.3 KiB
Average record size in memory152.3 B

Variable types

Text1
Numeric8
Categorical10

Alerts

Income has 39 (7.8%) missing values Missing
Loan_Balance has 29 (5.8%) missing values Missing
Customer_ID has unique values Unique
Missed_Payments has 77 (15.4%) zeros Zeros
Account_Tenure has 28 (5.6%) zeros Zeros

Reproduction

Analysis started2025-09-16 10:23:57.014296
Analysis finished2025-09-16 10:24:10.641599
Duration13.63 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Customer_ID
Text

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2025-09-16T13:24:11.057094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowCUST0001
2nd rowCUST0002
3rd rowCUST0003
4th rowCUST0004
5th rowCUST0005
ValueCountFrequency (%)
cust0001 1
 
0.2%
cust0016 1
 
0.2%
cust0003 1
 
0.2%
cust0004 1
 
0.2%
cust0005 1
 
0.2%
cust0006 1
 
0.2%
cust0007 1
 
0.2%
cust0008 1
 
0.2%
cust0009 1
 
0.2%
cust0010 1
 
0.2%
Other values (490) 490
98.0%
2025-09-16T13:24:11.660394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 699
17.5%
C 500
12.5%
U 500
12.5%
S 500
12.5%
T 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
4 200
 
5.0%
2 200
 
5.0%
5 101
 
2.5%
Other values (4) 400
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 699
17.5%
C 500
12.5%
U 500
12.5%
S 500
12.5%
T 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
4 200
 
5.0%
2 200
 
5.0%
5 101
 
2.5%
Other values (4) 400
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 699
17.5%
C 500
12.5%
U 500
12.5%
S 500
12.5%
T 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
4 200
 
5.0%
2 200
 
5.0%
5 101
 
2.5%
Other values (4) 400
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 699
17.5%
C 500
12.5%
U 500
12.5%
S 500
12.5%
T 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
4 200
 
5.0%
2 200
 
5.0%
5 101
 
2.5%
Other values (4) 400
10.0%

Age
Real number (ℝ)

Distinct57
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.266
Minimum18
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-16T13:24:11.879555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q133
median46.5
Q359.25
95-th percentile71
Maximum74
Range56
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation16.187629
Coefficient of variation (CV)0.34988174
Kurtosis-1.1017878
Mean46.266
Median Absolute Deviation (MAD)13.5
Skewness-0.077671867
Sum23133
Variance262.03932
MonotonicityNot monotonic
2025-09-16T13:24:12.058899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 18
 
3.6%
41 15
 
3.0%
52 15
 
3.0%
19 14
 
2.8%
49 13
 
2.6%
69 12
 
2.4%
45 12
 
2.4%
66 12
 
2.4%
61 12
 
2.4%
56 12
 
2.4%
Other values (47) 365
73.0%
ValueCountFrequency (%)
18 9
1.8%
19 14
2.8%
20 11
2.2%
21 7
1.4%
22 5
 
1.0%
23 8
1.6%
24 9
1.8%
25 12
2.4%
26 9
1.8%
27 1
 
0.2%
ValueCountFrequency (%)
74 8
1.6%
73 4
 
0.8%
72 9
1.8%
71 10
2.0%
70 8
1.6%
69 12
2.4%
68 10
2.0%
67 5
1.0%
66 12
2.4%
65 11
2.2%

Income
Real number (ℝ)

Missing 

Distinct271
Distinct (%)58.8%
Missing39
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean108379.89
Minimum15404
Maximum199943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-16T13:24:12.339743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15404
5-th percentile26174
Q162295
median107658
Q3155734
95-th percentile191615
Maximum199943
Range184539
Interquartile range (IQR)93439

Descriptive statistics

Standard deviation53662.724
Coefficient of variation (CV)0.49513542
Kurtosis-1.2030848
Mean108379.89
Median Absolute Deviation (MAD)46829
Skewness0.046086509
Sum49963131
Variance2.8796879 × 109
MonotonicityNot monotonic
2025-09-16T13:24:12.537218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90353 6
 
1.2%
42350 5
 
1.0%
116570 5
 
1.0%
70479 4
 
0.8%
38049 4
 
0.8%
81234 4
 
0.8%
115235 4
 
0.8%
53513 4
 
0.8%
165580 4
 
0.8%
149415 4
 
0.8%
Other values (261) 417
83.4%
(Missing) 39
 
7.8%
ValueCountFrequency (%)
15404 2
0.4%
16015 1
 
0.2%
16062 3
0.6%
16252 1
 
0.2%
18267 1
 
0.2%
18709 2
0.4%
19748 1
 
0.2%
19835 3
0.6%
20569 2
0.4%
21801 1
 
0.2%
ValueCountFrequency (%)
199943 1
 
0.2%
199402 3
0.6%
199294 1
 
0.2%
198401 3
0.6%
198062 2
0.4%
196415 2
0.4%
194819 1
 
0.2%
194208 1
 
0.2%
193998 2
0.4%
193677 2
0.4%

Credit_Score
Real number (ℝ)

Distinct234
Distinct (%)47.0%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean577.71687
Minimum301
Maximum847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-16T13:24:12.735688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile320.85
Q1418.25
median586
Q3727.25
95-th percentile824.15
Maximum847
Range546
Interquartile range (IQR)309

Descriptive statistics

Standard deviation168.88121
Coefficient of variation (CV)0.29232522
Kurtosis-1.3098219
Mean577.71687
Median Absolute Deviation (MAD)154.5
Skewness-0.034055608
Sum287703
Variance28520.863
MonotonicityNot monotonic
2025-09-16T13:24:12.919088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
823 10
 
2.0%
339 7
 
1.4%
820 7
 
1.4%
700 7
 
1.4%
383 6
 
1.2%
401 6
 
1.2%
316 6
 
1.2%
766 6
 
1.2%
617 6
 
1.2%
622 5
 
1.0%
Other values (224) 432
86.4%
ValueCountFrequency (%)
301 1
 
0.2%
302 1
 
0.2%
306 3
0.6%
307 2
 
0.4%
308 1
 
0.2%
310 4
0.8%
311 1
 
0.2%
315 1
 
0.2%
316 6
1.2%
319 2
 
0.4%
ValueCountFrequency (%)
847 4
0.8%
845 1
 
0.2%
843 1
 
0.2%
837 1
 
0.2%
836 5
1.0%
835 3
0.6%
834 2
 
0.4%
831 1
 
0.2%
826 4
0.8%
825 3
0.6%

Credit_Utilization
Real number (ℝ)

Distinct492
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49144591
Minimum0.05
Maximum1.0258425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-16T13:24:13.106451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.18654522
Q10.35648605
median0.48563558
Q30.63444035
95-th percentile0.82812155
Maximum1.0258425
Range0.97584253
Interquartile range (IQR)0.2779543

Descriptive statistics

Standard deviation0.19710258
Coefficient of variation (CV)0.40106669
Kurtosis-0.22059895
Mean0.49144591
Median Absolute Deviation (MAD)0.13641081
Skewness0.11986474
Sum245.72295
Variance0.038849427
MonotonicityNot monotonic
2025-09-16T13:24:13.422482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 9
 
1.8%
0.390501929 1
 
0.2%
0.519981109 1
 
0.2%
0.452140645 1
 
0.2%
0.732044381 1
 
0.2%
0.549055568 1
 
0.2%
0.486694296 1
 
0.2%
0.361467688 1
 
0.2%
0.677266039 1
 
0.2%
0.496336628 1
 
0.2%
Other values (482) 482
96.4%
ValueCountFrequency (%)
0.05 9
1.8%
0.059192485 1
 
0.2%
0.065235749 1
 
0.2%
0.066870954 1
 
0.2%
0.073373393 1
 
0.2%
0.10517251 1
 
0.2%
0.109776305 1
 
0.2%
0.12635992 1
 
0.2%
0.135619053 1
 
0.2%
0.144273833 1
 
0.2%
ValueCountFrequency (%)
1.025842526 1
0.2%
1.025017268 1
0.2%
1.008733579 1
0.2%
1.002481515 1
0.2%
0.972726689 1
0.2%
0.971116052 1
0.2%
0.954173056 1
0.2%
0.943684285 1
0.2%
0.934747675 1
0.2%
0.915989185 1
0.2%

Missed_Payments
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.968
Minimum0
Maximum6
Zeros77
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-16T13:24:13.582204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9469353
Coefficient of variation (CV)0.65597551
Kurtosis-1.145875
Mean2.968
Median Absolute Deviation (MAD)2
Skewness-0.036642686
Sum1484
Variance3.7905571
MonotonicityNot monotonic
2025-09-16T13:24:13.711404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 83
16.6%
2 79
15.8%
3 78
15.6%
0 77
15.4%
5 68
13.6%
6 61
12.2%
1 54
10.8%
ValueCountFrequency (%)
0 77
15.4%
1 54
10.8%
2 79
15.8%
3 78
15.6%
4 83
16.6%
5 68
13.6%
6 61
12.2%
ValueCountFrequency (%)
6 61
12.2%
5 68
13.6%
4 83
16.6%
3 78
15.6%
2 79
15.8%
1 54
10.8%
0 77
15.4%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
420 
1
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 420
84.0%
1 80
 
16.0%

Length

2025-09-16T13:24:13.872768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:14.034387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 420
84.0%
1 80
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 420
84.0%
1 80
 
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 420
84.0%
1 80
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 420
84.0%
1 80
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 420
84.0%
1 80
 
16.0%

Loan_Balance
Real number (ℝ)

Missing 

Distinct300
Distinct (%)63.7%
Missing29
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean48654.429
Minimum612
Maximum99620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-16T13:24:14.195083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum612
5-th percentile4627.5
Q123716.5
median45776
Q375546.5
95-th percentile95267
Maximum99620
Range99008
Interquartile range (IQR)51830

Descriptive statistics

Standard deviation29395.537
Coefficient of variation (CV)0.60416981
Kurtosis-1.239953
Mean48654.429
Median Absolute Deviation (MAD)25812
Skewness0.10519664
Sum22916236
Variance8.6409761 × 108
MonotonicityNot monotonic
2025-09-16T13:24:14.390263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82909 6
 
1.2%
10778 5
 
1.0%
13355 4
 
0.8%
62010 4
 
0.8%
62253 4
 
0.8%
99005 4
 
0.8%
11061 4
 
0.8%
13761 4
 
0.8%
98999 4
 
0.8%
17401 3
 
0.6%
Other values (290) 429
85.8%
(Missing) 29
 
5.8%
ValueCountFrequency (%)
612 1
 
0.2%
692 1
 
0.2%
1217 2
0.4%
1514 1
 
0.2%
1806 1
 
0.2%
2200 3
0.6%
2420 1
 
0.2%
2593 1
 
0.2%
2687 2
0.4%
2882 1
 
0.2%
ValueCountFrequency (%)
99620 1
 
0.2%
99005 4
0.8%
98999 4
0.8%
98062 1
 
0.2%
96507 2
0.4%
96485 1
 
0.2%
96167 1
 
0.2%
96056 1
 
0.2%
95930 1
 
0.2%
95376 2
0.4%

Debt_to_Income_Ratio
Real number (ℝ)

Distinct487
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29886164
Minimum0.1
Maximum0.55295645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-16T13:24:14.590766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.13941934
Q10.23363875
median0.30163394
Q30.36273739
95-th percentile0.45653279
Maximum0.55295645
Range0.45295645
Interquartile range (IQR)0.12909864

Descriptive statistics

Standard deviation0.09452091
Coefficient of variation (CV)0.31626979
Kurtosis-0.33460702
Mean0.29886164
Median Absolute Deviation (MAD)0.065319479
Skewness0.057659352
Sum149.43082
Variance0.0089342023
MonotonicityNot monotonic
2025-09-16T13:24:14.783124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 14
 
2.8%
0.317396115 1
 
0.2%
0.249608053 1
 
0.2%
0.278357197 1
 
0.2%
0.262904198 1
 
0.2%
0.193255565 1
 
0.2%
0.230000821 1
 
0.2%
0.269109247 1
 
0.2%
0.183806751 1
 
0.2%
0.189474358 1
 
0.2%
Other values (477) 477
95.4%
ValueCountFrequency (%)
0.1 14
2.8%
0.100372374 1
 
0.2%
0.104839281 1
 
0.2%
0.11476729 1
 
0.2%
0.123863322 1
 
0.2%
0.126318359 1
 
0.2%
0.127802704 1
 
0.2%
0.131529931 1
 
0.2%
0.13265152 1
 
0.2%
0.133071065 1
 
0.2%
ValueCountFrequency (%)
0.552956451 1
0.2%
0.549681784 1
0.2%
0.529397728 1
0.2%
0.526571269 1
0.2%
0.522601889 1
0.2%
0.510583483 1
0.2%
0.508126963 1
0.2%
0.494266812 1
0.2%
0.489641241 1
0.2%
0.48873359 1
0.2%
Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Unemployed
93 
retired
87 
Employed
82 
EMP
81 
Self-employed
80 

Length

Max length13
Median length10
Mean length8.188
Min length3

Characters and Unicode

Total characters4094
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEMP
2nd rowSelf-employed
3rd rowSelf-employed
4th rowUnemployed
5th rowSelf-employed

Common Values

ValueCountFrequency (%)
Unemployed 93
18.6%
retired 87
17.4%
Employed 82
16.4%
EMP 81
16.2%
Self-employed 80
16.0%
employed 77
15.4%

Length

2025-09-16T13:24:14.953193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:15.125154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
employed 159
31.8%
unemployed 93
18.6%
retired 87
17.4%
emp 81
16.2%
self-employed 80
16.0%

Most occurring characters

ValueCountFrequency (%)
e 836
20.4%
d 419
10.2%
l 412
10.1%
m 332
 
8.1%
p 332
 
8.1%
o 332
 
8.1%
y 332
 
8.1%
r 174
 
4.3%
E 163
 
4.0%
U 93
 
2.3%
Other values (8) 669
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 836
20.4%
d 419
10.2%
l 412
10.1%
m 332
 
8.1%
p 332
 
8.1%
o 332
 
8.1%
y 332
 
8.1%
r 174
 
4.3%
E 163
 
4.0%
U 93
 
2.3%
Other values (8) 669
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 836
20.4%
d 419
10.2%
l 412
10.1%
m 332
 
8.1%
p 332
 
8.1%
o 332
 
8.1%
y 332
 
8.1%
r 174
 
4.3%
E 163
 
4.0%
U 93
 
2.3%
Other values (8) 669
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 836
20.4%
d 419
10.2%
l 412
10.1%
m 332
 
8.1%
p 332
 
8.1%
o 332
 
8.1%
y 332
 
8.1%
r 174
 
4.3%
E 163
 
4.0%
U 93
 
2.3%
Other values (8) 669
16.3%

Account_Tenure
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.74
Minimum0
Maximum19
Zeros28
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-09-16T13:24:15.297675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9230537
Coefficient of variation (CV)0.6081164
Kurtosis-1.2864266
Mean9.74
Median Absolute Deviation (MAD)5
Skewness-0.085425724
Sum4870
Variance35.082565
MonotonicityNot monotonic
2025-09-16T13:24:15.453183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
17 34
 
6.8%
16 32
 
6.4%
14 29
 
5.8%
0 28
 
5.6%
6 28
 
5.6%
1 27
 
5.4%
15 27
 
5.4%
8 27
 
5.4%
9 26
 
5.2%
18 25
 
5.0%
Other values (10) 217
43.4%
ValueCountFrequency (%)
0 28
5.6%
1 27
5.4%
2 22
4.4%
3 24
4.8%
4 22
4.4%
5 24
4.8%
6 28
5.6%
7 18
3.6%
8 27
5.4%
9 26
5.2%
ValueCountFrequency (%)
19 24
4.8%
18 25
5.0%
17 34
6.8%
16 32
6.4%
15 27
5.4%
14 29
5.8%
13 24
4.8%
12 25
5.0%
11 16
3.2%
10 18
3.6%

Credit_Card_Type
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Gold
118 
Student
112 
Business
108 
Standard
86 
Platinum
76 

Length

Max length8
Median length8
Mean length6.832
Min length4

Characters and Unicode

Total characters3416
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStudent
2nd rowStandard
3rd rowPlatinum
4th rowPlatinum
5th rowStandard

Common Values

ValueCountFrequency (%)
Gold 118
23.6%
Student 112
22.4%
Business 108
21.6%
Standard 86
17.2%
Platinum 76
15.2%

Length

2025-09-16T13:24:15.642366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:15.832562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gold 118
23.6%
student 112
22.4%
business 108
21.6%
standard 86
17.2%
platinum 76
15.2%

Most occurring characters

ValueCountFrequency (%)
d 402
11.8%
t 386
11.3%
n 382
11.2%
s 324
9.5%
u 296
8.7%
a 248
7.3%
e 220
 
6.4%
S 198
 
5.8%
l 194
 
5.7%
i 184
 
5.4%
Other values (6) 582
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 402
11.8%
t 386
11.3%
n 382
11.2%
s 324
9.5%
u 296
8.7%
a 248
7.3%
e 220
 
6.4%
S 198
 
5.8%
l 194
 
5.7%
i 184
 
5.4%
Other values (6) 582
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 402
11.8%
t 386
11.3%
n 382
11.2%
s 324
9.5%
u 296
8.7%
a 248
7.3%
e 220
 
6.4%
S 198
 
5.8%
l 194
 
5.7%
i 184
 
5.4%
Other values (6) 582
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 402
11.8%
t 386
11.3%
n 382
11.2%
s 324
9.5%
u 296
8.7%
a 248
7.3%
e 220
 
6.4%
S 198
 
5.8%
l 194
 
5.7%
i 184
 
5.4%
Other values (6) 582
17.0%

Location
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Los Angeles
107 
Phoenix
103 
Chicago
103 
Houston
95 
New York
92 

Length

Max length11
Median length7
Mean length8.04
Min length7

Characters and Unicode

Total characters4020
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLos Angeles
2nd rowPhoenix
3rd rowChicago
4th rowPhoenix
5th rowPhoenix

Common Values

ValueCountFrequency (%)
Los Angeles 107
21.4%
Phoenix 103
20.6%
Chicago 103
20.6%
Houston 95
19.0%
New York 92
18.4%

Length

2025-09-16T13:24:16.018551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:16.203664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
los 107
15.3%
angeles 107
15.3%
phoenix 103
14.7%
chicago 103
14.7%
houston 95
13.6%
new 92
13.2%
york 92
13.2%

Most occurring characters

ValueCountFrequency (%)
o 595
14.8%
e 409
 
10.2%
s 309
 
7.7%
n 305
 
7.6%
g 210
 
5.2%
h 206
 
5.1%
i 206
 
5.1%
199
 
5.0%
L 107
 
2.7%
A 107
 
2.7%
Other values (14) 1367
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 595
14.8%
e 409
 
10.2%
s 309
 
7.7%
n 305
 
7.6%
g 210
 
5.2%
h 206
 
5.1%
i 206
 
5.1%
199
 
5.0%
L 107
 
2.7%
A 107
 
2.7%
Other values (14) 1367
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 595
14.8%
e 409
 
10.2%
s 309
 
7.7%
n 305
 
7.6%
g 210
 
5.2%
h 206
 
5.1%
i 206
 
5.1%
199
 
5.0%
L 107
 
2.7%
A 107
 
2.7%
Other values (14) 1367
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 595
14.8%
e 409
 
10.2%
s 309
 
7.7%
n 305
 
7.6%
g 210
 
5.2%
h 206
 
5.1%
i 206
 
5.1%
199
 
5.0%
L 107
 
2.7%
A 107
 
2.7%
Other values (14) 1367
34.0%

Month_1
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
On-time
177 
Missed
164 
Late
159 

Length

Max length7
Median length6
Mean length5.718
Min length4

Characters and Unicode

Total characters2859
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLate
2nd rowMissed
3rd rowMissed
4th rowLate
5th rowMissed

Common Values

ValueCountFrequency (%)
On-time 177
35.4%
Missed 164
32.8%
Late 159
31.8%

Length

2025-09-16T13:24:16.381985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:16.557627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
on-time 177
35.4%
missed 164
32.8%
late 159
31.8%

Most occurring characters

ValueCountFrequency (%)
e 500
17.5%
i 341
11.9%
t 336
11.8%
s 328
11.5%
O 177
 
6.2%
n 177
 
6.2%
- 177
 
6.2%
m 177
 
6.2%
M 164
 
5.7%
d 164
 
5.7%
Other values (2) 318
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2859
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 500
17.5%
i 341
11.9%
t 336
11.8%
s 328
11.5%
O 177
 
6.2%
n 177
 
6.2%
- 177
 
6.2%
m 177
 
6.2%
M 164
 
5.7%
d 164
 
5.7%
Other values (2) 318
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2859
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 500
17.5%
i 341
11.9%
t 336
11.8%
s 328
11.5%
O 177
 
6.2%
n 177
 
6.2%
- 177
 
6.2%
m 177
 
6.2%
M 164
 
5.7%
d 164
 
5.7%
Other values (2) 318
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2859
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 500
17.5%
i 341
11.9%
t 336
11.8%
s 328
11.5%
O 177
 
6.2%
n 177
 
6.2%
- 177
 
6.2%
m 177
 
6.2%
M 164
 
5.7%
d 164
 
5.7%
Other values (2) 318
11.1%

Month_2
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Late
173 
Missed
167 
On-time
160 

Length

Max length7
Median length6
Mean length5.628
Min length4

Characters and Unicode

Total characters2814
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLate
2nd rowMissed
3rd rowLate
4th rowMissed
5th rowOn-time

Common Values

ValueCountFrequency (%)
Late 173
34.6%
Missed 167
33.4%
On-time 160
32.0%

Length

2025-09-16T13:24:16.703984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:16.874755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
late 173
34.6%
missed 167
33.4%
on-time 160
32.0%

Most occurring characters

ValueCountFrequency (%)
e 500
17.8%
s 334
11.9%
t 333
11.8%
i 327
11.6%
L 173
 
6.1%
a 173
 
6.1%
M 167
 
5.9%
d 167
 
5.9%
O 160
 
5.7%
n 160
 
5.7%
Other values (2) 320
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 500
17.8%
s 334
11.9%
t 333
11.8%
i 327
11.6%
L 173
 
6.1%
a 173
 
6.1%
M 167
 
5.9%
d 167
 
5.9%
O 160
 
5.7%
n 160
 
5.7%
Other values (2) 320
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 500
17.8%
s 334
11.9%
t 333
11.8%
i 327
11.6%
L 173
 
6.1%
a 173
 
6.1%
M 167
 
5.9%
d 167
 
5.9%
O 160
 
5.7%
n 160
 
5.7%
Other values (2) 320
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 500
17.8%
s 334
11.9%
t 333
11.8%
i 327
11.6%
L 173
 
6.1%
a 173
 
6.1%
M 167
 
5.9%
d 167
 
5.9%
O 160
 
5.7%
n 160
 
5.7%
Other values (2) 320
11.4%

Month_3
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Late
169 
On-time
169 
Missed
162 

Length

Max length7
Median length6
Mean length5.662
Min length4

Characters and Unicode

Total characters2831
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissed
2nd rowLate
3rd rowLate
4th rowLate
5th rowMissed

Common Values

ValueCountFrequency (%)
Late 169
33.8%
On-time 169
33.8%
Missed 162
32.4%

Length

2025-09-16T13:24:17.018563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:17.190808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
late 169
33.8%
on-time 169
33.8%
missed 162
32.4%

Most occurring characters

ValueCountFrequency (%)
e 500
17.7%
t 338
11.9%
i 331
11.7%
s 324
11.4%
L 169
 
6.0%
a 169
 
6.0%
O 169
 
6.0%
n 169
 
6.0%
- 169
 
6.0%
m 169
 
6.0%
Other values (2) 324
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2831
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 500
17.7%
t 338
11.9%
i 331
11.7%
s 324
11.4%
L 169
 
6.0%
a 169
 
6.0%
O 169
 
6.0%
n 169
 
6.0%
- 169
 
6.0%
m 169
 
6.0%
Other values (2) 324
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2831
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 500
17.7%
t 338
11.9%
i 331
11.7%
s 324
11.4%
L 169
 
6.0%
a 169
 
6.0%
O 169
 
6.0%
n 169
 
6.0%
- 169
 
6.0%
m 169
 
6.0%
Other values (2) 324
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2831
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 500
17.7%
t 338
11.9%
i 331
11.7%
s 324
11.4%
L 169
 
6.0%
a 169
 
6.0%
O 169
 
6.0%
n 169
 
6.0%
- 169
 
6.0%
m 169
 
6.0%
Other values (2) 324
11.4%

Month_4
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Late
181 
Missed
160 
On-time
159 

Length

Max length7
Median length6
Mean length5.594
Min length4

Characters and Unicode

Total characters2797
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLate
2nd rowMissed
3rd rowOn-time
4th rowMissed
5th rowLate

Common Values

ValueCountFrequency (%)
Late 181
36.2%
Missed 160
32.0%
On-time 159
31.8%

Length

2025-09-16T13:24:17.348012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:17.514135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
late 181
36.2%
missed 160
32.0%
on-time 159
31.8%

Most occurring characters

ValueCountFrequency (%)
e 500
17.9%
t 340
12.2%
s 320
11.4%
i 319
11.4%
L 181
 
6.5%
a 181
 
6.5%
M 160
 
5.7%
d 160
 
5.7%
O 159
 
5.7%
n 159
 
5.7%
Other values (2) 318
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 500
17.9%
t 340
12.2%
s 320
11.4%
i 319
11.4%
L 181
 
6.5%
a 181
 
6.5%
M 160
 
5.7%
d 160
 
5.7%
O 159
 
5.7%
n 159
 
5.7%
Other values (2) 318
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 500
17.9%
t 340
12.2%
s 320
11.4%
i 319
11.4%
L 181
 
6.5%
a 181
 
6.5%
M 160
 
5.7%
d 160
 
5.7%
O 159
 
5.7%
n 159
 
5.7%
Other values (2) 318
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 500
17.9%
t 340
12.2%
s 320
11.4%
i 319
11.4%
L 181
 
6.5%
a 181
 
6.5%
M 160
 
5.7%
d 160
 
5.7%
O 159
 
5.7%
n 159
 
5.7%
Other values (2) 318
11.4%

Month_5
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Missed
187 
On-time
162 
Late
151 

Length

Max length7
Median length6
Mean length5.72
Min length4

Characters and Unicode

Total characters2860
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissed
2nd rowOn-time
3rd rowMissed
4th rowLate
5th rowLate

Common Values

ValueCountFrequency (%)
Missed 187
37.4%
On-time 162
32.4%
Late 151
30.2%

Length

2025-09-16T13:24:17.673380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:17.848958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
missed 187
37.4%
on-time 162
32.4%
late 151
30.2%

Most occurring characters

ValueCountFrequency (%)
e 500
17.5%
s 374
13.1%
i 349
12.2%
t 313
10.9%
M 187
 
6.5%
d 187
 
6.5%
O 162
 
5.7%
n 162
 
5.7%
- 162
 
5.7%
m 162
 
5.7%
Other values (2) 302
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 500
17.5%
s 374
13.1%
i 349
12.2%
t 313
10.9%
M 187
 
6.5%
d 187
 
6.5%
O 162
 
5.7%
n 162
 
5.7%
- 162
 
5.7%
m 162
 
5.7%
Other values (2) 302
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 500
17.5%
s 374
13.1%
i 349
12.2%
t 313
10.9%
M 187
 
6.5%
d 187
 
6.5%
O 162
 
5.7%
n 162
 
5.7%
- 162
 
5.7%
m 162
 
5.7%
Other values (2) 302
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 500
17.5%
s 374
13.1%
i 349
12.2%
t 313
10.9%
M 187
 
6.5%
d 187
 
6.5%
O 162
 
5.7%
n 162
 
5.7%
- 162
 
5.7%
m 162
 
5.7%
Other values (2) 302
10.6%

Month_6
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Late
172 
Missed
168 
On-time
160 

Length

Max length7
Median length6
Mean length5.632
Min length4

Characters and Unicode

Total characters2816
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLate
2nd rowOn-time
3rd rowLate
4th rowLate
5th rowLate

Common Values

ValueCountFrequency (%)
Late 172
34.4%
Missed 168
33.6%
On-time 160
32.0%

Length

2025-09-16T13:24:18.004224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-16T13:24:18.185765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
late 172
34.4%
missed 168
33.6%
on-time 160
32.0%

Most occurring characters

ValueCountFrequency (%)
e 500
17.8%
s 336
11.9%
t 332
11.8%
i 328
11.6%
L 172
 
6.1%
a 172
 
6.1%
M 168
 
6.0%
d 168
 
6.0%
O 160
 
5.7%
n 160
 
5.7%
Other values (2) 320
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 500
17.8%
s 336
11.9%
t 332
11.8%
i 328
11.6%
L 172
 
6.1%
a 172
 
6.1%
M 168
 
6.0%
d 168
 
6.0%
O 160
 
5.7%
n 160
 
5.7%
Other values (2) 320
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 500
17.8%
s 336
11.9%
t 332
11.8%
i 328
11.6%
L 172
 
6.1%
a 172
 
6.1%
M 168
 
6.0%
d 168
 
6.0%
O 160
 
5.7%
n 160
 
5.7%
Other values (2) 320
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 500
17.8%
s 336
11.9%
t 332
11.8%
i 328
11.6%
L 172
 
6.1%
a 172
 
6.1%
M 168
 
6.0%
d 168
 
6.0%
O 160
 
5.7%
n 160
 
5.7%
Other values (2) 320
11.4%

Interactions

2025-09-16T13:24:08.417831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:23:58.862872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:00.158321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:01.454568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:02.784147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:04.321492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:05.841342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:07.169396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:08.572909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:23:59.029562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:00.318030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:01.600239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:02.994049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:04.514531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:05.998252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:07.317144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:08.737789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:23:59.188574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:00.497858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:01.777767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:03.198501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:04.735248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:06.160152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:07.481117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:08.898236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:23:59.404439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:00.658175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:02.003162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:03.385001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:04.911179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:06.311250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:07.632508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:09.057019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:23:59.551194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:00.813344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:02.174882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:03.566074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:05.098999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:06.452914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:07.795628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:09.220761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:23:59.707816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:00.977215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:02.323945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:03.780503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:05.306444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:06.609499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:07.952265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:09.375052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:23:59.847222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:01.135502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:02.475427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:03.958463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:05.493943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:06.750504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:08.117822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:09.535941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:23:59.993266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:01.290256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:02.626495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:04.123023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:05.672644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:06.894058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-09-16T13:24:08.258257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2025-09-16T13:24:18.389754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Account_TenureAgeCredit_Card_TypeCredit_ScoreCredit_UtilizationDebt_to_Income_RatioDelinquent_AccountEmployment_StatusIncomeLoan_BalanceLocationMissed_PaymentsMonth_1Month_2Month_3Month_4Month_5Month_6
Account_Tenure1.0000.0140.000-0.0320.0680.0230.0880.000-0.0000.0560.000-0.0980.0450.0000.0820.0000.0340.017
Age0.0141.0000.000-0.0330.018-0.0030.0320.0000.005-0.0540.000-0.0250.0000.0190.1100.1210.1110.047
Credit_Card_Type0.0000.0001.0000.0000.0000.0000.0280.0000.0000.0000.0000.0020.0500.0000.0900.0000.0540.000
Credit_Score-0.032-0.0330.0001.000-0.0240.0090.0240.0400.071-0.0180.000-0.0120.0000.0000.0000.0560.0870.000
Credit_Utilization0.0680.0180.000-0.0241.000-0.0630.0000.0000.054-0.0650.0560.0220.0340.0000.0000.0000.0000.000
Debt_to_Income_Ratio0.023-0.0030.0000.009-0.0631.0000.0000.000-0.0730.0570.084-0.0100.0750.0000.0890.0000.0000.080
Delinquent_Account0.0880.0320.0280.0240.0000.0001.0000.0000.0000.0000.0000.0000.0710.0000.0000.0660.0150.000
Employment_Status0.0000.0000.0000.0400.0000.0000.0001.0000.0000.0770.0000.0000.0000.0370.0450.0140.0260.000
Income-0.0000.0050.0000.0710.054-0.0730.0000.0001.000-0.0470.000-0.0050.0800.1040.0000.0640.0000.000
Loan_Balance0.056-0.0540.000-0.018-0.0650.0570.0000.077-0.0471.0000.030-0.0100.0000.0370.0000.0900.0630.000
Location0.0000.0000.0000.0000.0560.0840.0000.0000.0000.0301.0000.0000.0000.0000.0000.0510.0000.100
Missed_Payments-0.098-0.0250.002-0.0120.022-0.0100.0000.000-0.005-0.0100.0001.0000.0580.0000.0000.0000.0000.088
Month_10.0450.0000.0500.0000.0340.0750.0710.0000.0800.0000.0000.0581.0000.0000.0000.0000.0460.000
Month_20.0000.0190.0000.0000.0000.0000.0000.0370.1040.0370.0000.0000.0001.0000.0000.0510.0260.064
Month_30.0820.1100.0900.0000.0000.0890.0000.0450.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
Month_40.0000.1210.0000.0560.0000.0000.0660.0140.0640.0900.0510.0000.0000.0510.0001.0000.0000.000
Month_50.0340.1110.0540.0870.0000.0000.0150.0260.0000.0630.0000.0000.0460.0260.0000.0001.0000.094
Month_60.0170.0470.0000.0000.0000.0800.0000.0000.0000.0000.1000.0880.0000.0640.0000.0000.0941.000

Missing values

2025-09-16T13:24:09.828029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-16T13:24:10.258109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-16T13:24:10.542495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Customer_IDAgeIncomeCredit_ScoreCredit_UtilizationMissed_PaymentsDelinquent_AccountLoan_BalanceDebt_to_Income_RatioEmployment_StatusAccount_TenureCredit_Card_TypeLocationMonth_1Month_2Month_3Month_4Month_5Month_6
0CUST000156165580.0398.00.3905023016310.00.317396EMP18StudentLos AngelesLateLateMissedLateMissedLate
1CUST000269100999.0493.00.3124446117401.00.196093Self-employed0StandardPhoenixMissedMissedLateMissedOn-timeOn-time
2CUST000346188416.0500.00.3599300013761.00.301655Self-employed1PlatinumChicagoMissedLateLateOn-timeMissedLate
3CUST000432101672.0413.00.3714003088778.00.264794Unemployed15PlatinumPhoenixLateMissedLateMissedLateLate
4CUST00056038524.0487.00.2347162013316.00.510583Self-employed11StandardPhoenixMissedOn-timeMissedLateLateLate
5CUST00062584042.0700.00.6505406048361.00.260688Unemployed7GoldNew YorkOn-timeLateMissedMissedMissedLate
6CUST00073835056.0354.00.390581304638.00.484265employed17PlatinumNew YorkOn-timeMissedMissedLateMissedLate
7CUST000856123215.0415.00.5327155055776.00.358695EMP1StudentNew YorkOn-timeOn-timeOn-timeLateMissedLate
8CUST00093666991.0405.00.41303551NaN0.219854Employed12StudentPhoenixOn-timeOn-timeOn-timeMissedLateOn-time
9CUST00104034870.0679.00.3618244093922.00.333081EMP5BusinessLos AngelesOn-timeMissedMissedOn-timeMissedMissed
Customer_IDAgeIncomeCredit_ScoreCredit_UtilizationMissed_PaymentsDelinquent_AccountLoan_BalanceDebt_to_Income_RatioEmployment_StatusAccount_TenureCredit_Card_TypeLocationMonth_1Month_2Month_3Month_4Month_5Month_6
490CUST04916654954.0825.00.7615162077743.00.378082Self-employed16PlatinumChicagoLateMissedOn-timeOn-timeMissedOn-time
491CUST049269177993.0818.00.4245440032630.00.237540employed17StandardNew YorkMissedMissedMissedLateLateOn-time
492CUST049359191517.0813.00.4244705023047.00.311788Unemployed2PlatinumPhoenixMissedMissedLateLateOn-timeLate
493CUST049423168487.0306.00.496720605539.00.282865Unemployed1StandardChicagoMissedOn-timeOn-timeOn-timeMissedOn-time
494CUST049532NaN811.00.4188756116699.00.412470Employed0BusinessChicagoLateLateLateOn-timeOn-timeOn-time
495CUST04967148307.0688.00.4865222012707.00.373033retired9BusinessPhoenixOn-timeOn-timeMissedOn-timeOn-timeLate
496CUST04976086180.0836.00.6081742145595.00.291943Unemployed18StudentHoustonOn-timeOn-timeLateLateLateMissed
497CUST049854152326.0847.00.6769500044449.00.104839Employed16StudentPhoenixOn-timeLateLateOn-timeLateMissed
498CUST049950105852.0343.00.7006432111155.00.236477Employed11StudentPhoenixLateOn-timeLateMissedOn-timeMissed
499CUST05002540945.0442.00.9113701036968.00.370422Self-employed0BusinessHoustonMissedLateLateOn-timeLateOn-time